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73f8503 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | # -*- coding: utf-8 -*-
"""Self-contained Kenga instruct encoder. Byte UTF-8, bidirectional.
Usage (retrieval, Giga-style prefix on the query only):
from modeling_kenga_embed import KengaEmbed
m = KengaEmbed.from_pretrained("GermannM/kenga-embed-prophet-instruct")
q = m.encode_queries(["Where is the capital of Russia?"])
p = m.encode_documents(["Moscow is the capital of Russia."])
STS (prefix on both sentences):
a = m.encode_sts(["The cat sits on the mat."])
b = m.encode_sts(["A cat is sitting on a mat."])
"""
import json
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
INSTR_RETR = "Instruct: Given a query, retrieve relevant passages\nQuery: "
INSTR_STS = "Instruct: Retrieve semantically similar text.\nQuery: "
class BL(nn.Module):
def __init__(self, in_f, out_f, k):
super().__init__()
kk = min(k, in_f, out_f)
self.U = nn.Parameter(torch.zeros(out_f, kk))
self.V = nn.Parameter(torch.zeros(in_f, kk))
self.S = nn.Parameter(torch.ones(kk))
self.b = nn.Parameter(torch.zeros(out_f))
self.register_buffer("mask", torch.ones(kk))
def set_active(self, k_t):
m = torch.zeros_like(self.mask)
m[: min(int(k_t), self.mask.numel())] = 1
self.mask.copy_(m)
def forward(self, x):
return ((x @ self.V) * (self.S * self.mask)) @ self.U.T + self.b
class BAttn(nn.Module):
def __init__(self, d, heads, k_attn):
super().__init__()
self.h = heads
self.dh = d // heads
self.scale = self.dh ** -0.5
self.q = BL(d, d, k_attn)
self.k_ = BL(d, d, k_attn)
self.v = BL(d, d, k_attn)
self.o = BL(d, d, k_attn)
def set_rank(self, ka):
for bl in (self.q, self.k_, self.v, self.o):
bl.set_active(ka)
def forward(self, x, pad):
B, T, _ = x.shape
Q = self.q(x).view(B, T, self.h, self.dh).transpose(1, 2)
K = self.k_(x).view(B, T, self.h, self.dh).transpose(1, 2)
V = self.v(x).view(B, T, self.h, self.dh).transpose(1, 2)
att = (Q.float() @ K.float().transpose(-2, -1)) * self.scale
att = att.masked_fill(pad, float("-inf")).softmax(-1)
return self.o((att @ V).transpose(1, 2).reshape(B, T, x.size(-1)))
class BBlock(nn.Module):
def __init__(self, d, dff, heads, k_attn, k_ff):
super().__init__()
self.ln1 = nn.LayerNorm(d)
self.ln2 = nn.LayerNorm(d)
self.attn = BAttn(d, heads, k_attn)
self.fc1 = BL(d, dff, k_ff)
self.fc2 = BL(dff, d, k_ff)
def set_rank(self, ka, kf):
self.attn.set_rank(ka)
self.fc1.set_active(kf)
self.fc2.set_active(kf)
def forward(self, x, pad):
x = x + self.attn(self.ln1(x), pad)
return x + self.fc2(F.gelu(self.fc1(self.ln2(x))))
class KengaEmbedEnc(nn.Module):
def __init__(self, d=768, layers=8, heads=12, dff=3072, seq=160,
v=256, k_attn=192, k_ff=512):
super().__init__()
self.d = d
self.seq = seq
self.tok = nn.Embedding(v, d)
self.pos = nn.Parameter(torch.zeros(1, seq, d))
self.blocks = nn.ModuleList(
[BBlock(d, dff, heads, k_attn, k_ff) for _ in range(layers)]
)
self.ln = nn.LayerNorm(d)
self.proj = nn.Linear(d, d)
def set_rank(self, ka, kf):
for b in self.blocks:
b.set_rank(ka, kf)
def forward(self, idx, lengths=None):
B, T = idx.shape
x = self.tok(idx) + self.pos[:, :T]
pad = idx.eq(0)
mask = pad[:, None, None, :].expand(B, 1, T, T)
for blk in self.blocks:
x = blk(x, mask)
x = self.ln(x)
m = (~pad).unsqueeze(-1).float()
pooled = (x * m).sum(1) / m.sum(1).clamp(min=1)
return F.normalize(self.proj(pooled), dim=-1)
class KengaEmbed:
def __init__(self, model, seq, instruct=True, device="cpu"):
self.model = model.to(device).eval()
self.seq = seq
self.instruct = instruct
self.device = device
@classmethod
def from_pretrained(cls, path, device="cpu"):
cfg = json.load(open(os.path.join(path, "config.json"), encoding="utf-8"))
m = KengaEmbedEnc(
d=cfg.get("d", 768),
layers=cfg.get("layers", 8),
heads=cfg.get("heads", 12),
dff=cfg.get("dff", 3072),
seq=cfg.get("seq", 160),
v=cfg.get("v", 256),
k_attn=cfg.get("k_attn", 192),
k_ff=cfg.get("k_ff", 512),
)
blob = torch.load(os.path.join(path, "pytorch_model.bin"),
map_location="cpu", weights_only=False)
sd = blob["model"] if isinstance(blob, dict) and "model" in blob else blob
m.load_state_dict(sd, strict=True)
rank = cfg.get("rank") or blob.get("rank")
if rank:
m.set_rank(*list(rank))
return cls(m, seq=cfg.get("seq", 160),
instruct=bool(cfg.get("instruct", True)), device=device)
def _ids(self, text):
raw = (text or "").encode("utf-8", errors="ignore")[: self.seq]
ids = [b if b else 1 for b in raw][: self.seq]
n = len(ids)
ids = ids + [0] * (self.seq - n)
return ids, n
def _prefix(self, text, kind):
if not self.instruct or not text:
return text
if text.startswith("Instruct:"):
return text
p = INSTR_STS if kind == "sts" else INSTR_RETR
return p + text
def encode(self, texts, kind="doc", bs=32):
vecs = []
with torch.no_grad():
for i in range(0, len(texts), bs):
chunk = texts[i : i + bs]
ids, ln = [], []
for t in chunk:
s = t if kind == "doc" else self._prefix(t, kind)
ii, n = self._ids(s)
ids.append(ii)
ln.append(n)
x = torch.tensor(ids, device=self.device)
l = torch.tensor(ln, device=self.device)
vecs.append(self.model(x, l).cpu())
return torch.cat(vecs, 0)
def encode_queries(self, texts, **kw):
return self.encode(texts, kind="retr", **kw)
def encode_documents(self, texts, **kw):
return self.encode(texts, kind="doc", **kw)
def encode_sts(self, texts, **kw):
return self.encode(texts, kind="sts", **kw)
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